Aggregate Insider Trading and the Predictability of Market Returns: Contrarian Strategy or Managerial Timing?

Xiao Q JiangAssistant Professor

Department of FinanceUniversity of Northern Iowa

Cedar Falls, IA 50614XQ.Jiang@uni.edu

Mir A. Zamana

Carl Schweser Professor of Financial AnalysisDepartment of Finance

University of Northern IowaCedar Falls, IA 50614Mir.Zaman@uni.edu

First draft: January 2007This draft: July, 2007

a Corresponding Author. Tel.: +1 319 273 2579; Fax: +1 319 273 2922

Aggregate Insider Trading and the Predictability of Market Returns: Contrarian Strategy or Managerial Timing?

Abstract

We decompose realized market returns into expected return, unexpected cash flow news and unexpected discount rate news to test the relation between aggregate market returns and aggregate insider trading. Our motivation is to distinguish whether the observed relation between market returns and insider trading is due to contrarian strategy or managerial timing. We find that (1) the predictive ability of aggregate insider trading is much stronger than what was reported in earlier studies (2) aggregate insider trading is strongly related to unexpected cash-flow news (3) market expectations do not cause insider trading contrary to what others have documented and (4) aggregate insider trading in firms with high information uncertainty is more likely to be associated with contrarian investment strategy. These results strongly suggest that the predictive ability of aggregate insider trading is because of managerial timing rather than contrarian strategy. These results hold even after we control for information uncertainty by using firm size as proxies.

2

Aggregate Insider Trading and the Predictability of Market Returns: Contrarian Strategy or Managerial Timing?

I. Introduction

Recent studies on aggregate insider trading have documented that insiders are able

to predict future market movements and that they are able to time the market (Seyhun

(1988), Lakonishok and Lee (2001)). However, it is not clear from the evidence

whether this predictability of market returns is due to insiders being contrarian

investors (Rozeff and Zaman (1998), Lakonishok and Lee (2001)) or whether

managers are better informed about their firm’s future prospects and it is this

information that explains their market timing ability (Ke, Huddart and Petroni (2003))

or whether it is a function of both (Piotroski and Roulstone (2005)).

There is substantial evidence that corporate officers and directors are able to

discern apparent mispricing in their firm’s securities based on firm related

information and are able to profitably trade on this.a If this information is related to

future economy wide activity then aggregate insider trading should predict future

market movements and the market timing ability of insiders would be based on

information unanticipated by the market (see Seyhun, 1988). We differentiate this

from the contrarian investment strategy of insiders and define it as managerial timing.

If insiders are motivated to trade because of perceived mispricing, it is also

conceivable they may react to market returns. It is possible that noise traders may

drive market prices way from intrinsic values even in the absence of new information.

a Previous studies based on US data unanimously documented that insiders are better informed and earn abnormal returns [Lorie and Niederhofer (1968), Jaffe (1974), Seyhun (1986), Rozeff and Zaman (1988), Lin and Howe (1990) and Lakonishok and Lee (2001)]. Using Oslo Stock Exchange data Eckbo and Smith (1998) show that insiders do not earn abnormal returns while Jeng, Metrick and Zeckhauser (2003) show that abnormal returns earned by insiders are restricted only to purchases.

3

Hence, a stock that was trading roughly at its intrinsic value could decline (rise)

significantly because of such noise trading. Corporate insiders may then perceive the

stock to be undervalued (overvalued) and buy (sell) it. To the extent that noise

trading is a market wide phenomenon, we would expect market returns to ‘predict’

aggregate insider transactions (See Rozeff and Zaman (1998), Chowdhury, Howe and

Lin (1993) and Lakonishok and Lee (2001). Such a relationship would be viewed as

insiders following a contrarian investment strategy. On the other hand, if mispricing

is firm specific then insiders’ transactions in each firm will cancel out and aggregate

insider trading should not be related to market returns. Even though under both

contrarian strategy and managerial timing insider trading is related to market returns,

the key distinction is that managerial timing implies insider trading will predict future

market returns while contrarian strategy implies insider trading is a reaction to market

returns.

Other related studies of managerial decisions also suggest that insiders are better

informed about their companies’ future prospects. For example, Ikenberry,

Lakonishok and Vermaelen (1995) find positive abnormal returns earned by

shareholders of companies that have announced open market share repurchases.

These abnormal returns persist for some time after the announcement. One of the

main motivations for repurchases seems to be that insiders perceive the company’s

stock as being undervalued. Loughran and Ritter (1995), on the other hand, observe a

prolonged underperformance by companies following seasoned equity offerings.

This is in line with the hypothesis that companies tend to issue seasoned equity when

they perceive the market to be too optimistic about the prospects of their company.

4

Baker and Wurgler (2000) find that the share of equity issues in total new equity and

debt issues increases right after a year of high market returns and has been a stable

predictor of U.S. stock market returns between 1928 and 1996. The paper also

provides evidence of issuing firms preferring equity finance before periods of low

market returns and shunning equity in favor of debt before periods of high market

returns. Overall, the results add to a growing body of evidence that managerial

decisions are in response to or in anticipation of market conditions (see also Baker,

Taliaferro and Wurgler, 2006 among others).

A related line of research on insider trading has focused on whether aggregate

insider trading can predict market movements and could be used as a tool to time the

market. Seyhun (1988) provides evidence suggesting that some of the mispricing

observed by insiders in their own firms’ securities is caused by unanticipated changes

in economy wide activity. In a related paper, Seyhun (1992) also finds that aggregate

insider transactions are correlated with the return on the market during the subsequent

two months of such transactions and provides evidence of relations between

aggregate insider trading and variables that are associated with business conditions

and fundamental values.

Chowdhury, Howe and Lin (1993) find that stock market returns Granger-causes

insider transactions while the predictive content of aggregate insider transactions for

subsequent market returns is slight. Lakonishok and Lee (2001) also provide

evidence in support of the predictive ability of aggregate insider trading and market

movement. They conclude that this ability is partially explained by their finding that

insiders act as contrarian investors.

5

Previous studies simply examine the relationship between realized market return

and some metric of insider trading without explicitly considering the source of

predictability. Piotroski and Roulstone (2005) is an exception. Their paper attempts

to differentiate the source of the predictability and find that insider trades are related

to the firm’s future earnings performance. However, they use the change in

accounting returns as proxies for future cash flows. Cohen, Gompers, and

Vuolteenaho (2002) point out that the change in accounting returns is not a good

measure to proxy future cash flows.

Both conclusions of contrarian strategy of investing by insiders and managerial

timing rely on insider trading to be positively related to subsequent realized market

returns. These studies, however, make no attempt to determine whether the apparent

predictability of market returns by aggregate insider trading is due to contrarian

strategy or managerial timing. The purpose of this paper is to re-examine the ability

of aggregate insider trading to predict market-wide movement using return

decomposition in a vector autoregressive (VAR) model framework. Such a re-

examination is called for because of mixed results reported in previous papers.

Moreover, it is important for the capital markets to be able to distinguish between

these two sources of predictability. If insiders are trading based on contrarian

strategy, then in aggregate, such trading would not provide any ‘new’ information

about the future economy-wide activity. Aggregate insider trading would in this case

imply market overreaction (under reaction) and subsequently lead to market

correction. However, if insiders are trading on the basis of managerial timing, then

aggregate insider trading will predict future real economic activities and future

6

market returns. In order to distinguish between these two sources of predictability we

closely follow Campbell (1991) and Hecht and Vuolteenaho (2005) method of

decomposing aggregate market return into expected return, unexpected cash-flow

news and unexpected discount rate news. We argue that managerial timing suggests a

positive relation between aggregate insider trading and unexpected cash-flow news

while contrarian strategy would suggest a negative relation between insider trading

and expected return. Using this decomposition, a regression of market returns on

insider trading measures is then decomposed into three component regressions. We

find the following: (1) the predictive ability of aggregate insider trading is much

stronger than what was reported in earlier studies (2) aggregate insider trading is

strongly related to unexpected cash-flow news (3) market expectations do not cause

insider trading, contrary to what others have documented and (4) aggregate insider

trading in firms with high information uncertainty is more likely to be associated with

contrarian investment strategy. These results strongly suggest that the predictive

ability of aggregate insider trading is because of managerial timing rather than

contrarian strategy.

Our contribution is two-fold. First, this paper provides definitive evidence into the

debate of whether insider trading based on perceived mispricing is a result of

contrarian investment strategy or whether it is based on insiders’ access to

information about future cash flow news. By decomposing realized market returns

into expected returns, unexpected cash flow news and unexpected changes in discount

rate this paper directly tests the sources of the insider trading predictability. Second,

this paper contributes to the existing literature on the importance of the relation

7

between corporate transactions and insiders ability to time the market. When the

firm’s securities are mispriced and insiders are able to identify this mispricing, then

this ability affect the financing, investment and other corporate transactions.

The paper is organized as follows. Section II discusses reasons to believe why

insider trading can predict future market returns, section III develops the framework

and formulates the hypotheses, section IV describes the data and provides summary

statistics. Results are reported and discussed in section V and VI while the last

section contains a summary and interpretation of the results.

II. The information content of aggregate insider trading: managerial timing

or contrarian strategy?

There are a number of compelling and competing reasons to believe that aggregate

insider trading can predict future market returns. Assume that company executives

and directors know their businesses more intimately than analysts (investors)

following their stocks. They know when demand for their goods and services are

increasing, when inventories are piling up, when production costs are increasing or

profit margins declining, etc. Given their knowledge about their firm, insiders should

be able to predict, say, if the firm’s future cash flows would increase and would buy

stocks in their firms. If the predicted increase in cash flows by insiders is strictly the

result of some firm-specific improvement (e.g. profit margin) there should be no

relation between insider trading and market return. On the other hand if the cash

flows are related to economy wide activity such as increases in aggregate demand of

goods and services then subsequently when the increase in economy wide activity is

8

recognized by the market, stock prices will rise. This will result in a positive relation

between insider trading and market return. We call this the managerial timing

hypothesis. Seyhun (1988, 1992) is the first study that documents aggregate insider

trading is positively related to market activity and in the latter paper provides

evidence of aggregate insider trading being related to macro-economic variables.

A competing hypothesis regarding aggregate insider trading relies on the

contrarian strategy of investing. If stock prices are affected by the trading of both

informed and uninformed (noise) traders then prices can diverge from fundamental

values (Shiller 1984, De Long et al 1990). According to this view noise traders may

drive market prices away from current fundamental values. However, in the long run

prices would revert back to fundamental values. If the contrarian strategy is

employed by insiders at the firm specific level then there should be no relation

between market returns and insider trading. On the other hand, if ‘noise’ trading is a

market wide phenomenon then a relation between aggregate insider trading and

market return should exist. In such a scenario, market returns would ‘predict’ insider

trading behavior. Chowdhury, Howe and Lin (1993) and Lakonishok and Lee (2001)

provide evidence in support of aggregate insider trading being driven by the

contrarian strategy.

III. Framework and hypotheses

In order to test the relation between aggregate stock returns and inside trading and

whether the relation is due to the contrarian strategy or managerial timing we use the

9

standard log-linear approximation of present value model developed by Campbell

(1991).

A. Log-linear present value model framework and insider trading

Campbell (1991) decomposes the realized return on equities into following three

components:

(1)

where R is the log return on equities, D is dividend growth, ρ is the discount factor,

Et(Rt+1) is the one-period expected return, NCF, t+1 is the cash flow news, and NDR, t+1 is

the discount rate news. This equation states that the realized return must be

associated with the expected return, the changes in expectations of future cash flows,

and/or the changes in the expectations of future discount rates. As emphasized by

Campbell (1991), equation (1) is really nothing more than a dynamic accounting

identity relating the current return innovation to revisions in expectation.

Hecht and Vuolteenaho (2005) apply this method to measure the relative

importance of these three effects in regressions of returns on cash flow proxies.

Based on the equation (1), the explanatory power of cash flow proxies may arise from

the correlation of cash flow proxies (predictors) with one-period expected returns,

cash flow news, and/or expected return news. They argue that “If expected-return

variation is responsible for the high explanatory power of the aggregate regressions,

these R2 should not be interpreted as evidence of cash-flow news driving the returns.

Similarly, if expected-return news is highly variable and positively correlated with

cash-flow news, the low R2s in regressions of firm-level returns on earnings do not

10

necessarily imply that earnings are a noisy or delayed measure of the cash-flow-

generating ability of the firm. Even if earnings are a clean signal of cash-flow news,

expected-return effects (due to variation in risk-adjusted discount rates and/or

mispricing) can garble the earnings-returns relation.”

In a similar spirit, we apply Campbell’s decomposition to estimate and test the

dynamic relation between aggregate market returns and aggregate insider trading.

This method uniquely helps us to distinguish whether the relation between aggregate

market returns and insider trading is due to a contrarian strategy or managerial timing.

Consider a typical forecast regression of returns on insider trading:

Rt+1 = α + βITt + et+1 (2)

where IT is a measure of insider trading. Seyhun (1988) uses a similar methodology

to show a weak relationship between insider trading and market returns and concludes

insider transaction predict market return. As analyzed above, it is difficult to interpret

the coefficient β, and more importantly, using regression (2) we cannot distinguish

whether the relation between aggregate market returns and insider trading is due to

the contrarian strategy or managerial timing.

Using Campbell’s (1991) decomposition, however, we can rewrite the

regression (2) as following:

EtRt+1 = α + βERITt + eER,t+1 (3a)

NCF,t+1 = α + βCFITt + eCF,t+1 (3b)

-NDR,t+1 = α + β-DRITt + e-DR,t+1 (3c)

11

Since the sum of the left-hand-side in regression (3) is the realized return and the

independent variable in regression (3) are same, regression (2) can also be expressed

as:

Rt+1 = α + ( βER+βCF+β-DR) ITt + (eER,t+1+ eCF,t+1+ e-DR,t+1) (4)

Regression (3) and (4) show that there are three sources driving the relation between

aggregate market return and insider trading: one-period expected return, cash flow

news, and discount rate news.

We also consider the following regression:

ITt+1 = α + γRt + ut+1 (5a)

ITt+1 = α + γEREt(Rt+1) + uER,t+1 (5b)

ITt+1 = α + γCF NCF,t + uCF,t+1 (5c)

ITt+1 = α + γ-DR(-NDR,t+1) + u-DR,t+1 (5d)

Equation (4) shows, if expected-return variation is responsible for the high

explanatory power of the aggregate regressions, these R2 should not be interpreted as

evidence of managerial timing driving the returns. Similarly if expected-return news

is highly variable and positively correlated with cash-flow news, the low R2s in

regressions of market returns on inside trading do not necessarily imply that insider

trading is a noisy or delayed measure of the cash-flow-generating ability of the firm.

Even if insider trading is a clean signal of cash-flow news, expected-return effects

(due to variation in risk-adjusted discount rates and/or mispricing) can garble the

insider trading-returns relation. We use regression (3) and (5) to estimate the relation

between aggregate market return and insider trading, and distinguish whether the

12

relation is attributed to the managerial timing (as evidenced in Seyhun (1988)) or

contrarian strategy as claimed by Chowdhury, Howe and Lin (1993) and Lakonishok

and Lee (2001).

Managerial timing implies that insiders are better able to predict future cash flow

news of the firm than outside investors. If these cash flows are related to economy

wide activity then subsequent to aggregate insider buying (selling) in stocks of their

firm the aggregate market returns should increase (decrease). It may be argued that if

insiders have information about their firm’s future cash flow news which is related to

economy wide activity then it is likely they may be better off trading in options or

other derivative securities than trading in stocks of their firm. However, given

Seyhun’s (1986) evidence of passive as well as active trading by insiders around

firm-specific nonpublic information insiders would also be expected to trade in stocks

of their firm. If the hypothesis of managerial timing is true then we expect positive

and significant coefficients for βCF and β-DR . In contrast, if insider trading do not

reveal information about future economy wide activity then the coefficients βCF and β-DR

will be insignificant. Furthermore, under the hypothesis of managerial timing if

insider managers know more about their cash flow news and in the aggregate, cash

flow news do not cancel out but rather are proxies of aggregate market cash flow

news, then the coefficient βCF should dominate β-DR.

Contrarian strategy implies that outsider investors make valuation errors through

the application of inferior valuation models and/or the incorporation of biased

judgments. Based on the perceived mispricing, insiders trade against outside

investors’ sentiment. If the contrarian strategy drives the relation between aggregate

13

market return and insider trading, we will expect that γER to be significantly negative.

For instance, if outside market expectation ET [Rt+1] is positive, and if inside traders

perceive that this expectation is wrong, insider traders will sell their stocks, i.e., γER is

negative. In this case note that the coefficients of cash flow news and discount rate

news should be insignificant. We formulate the following hypotheses:

Hypothesis 1: If insider trading is not informative (in terms of managerial timing)

then the lagged values of βCF, β-DR are indistinguishable from zero; otherwise they are

positive.

Hypothesis 2: If insider trading is not informative (in terms of contrarian strategy)

then the lagged values of γER are indistinguishable from zero; otherwise they are

negative.

In the following section we use Campbell (1991) decomposition method to

estimate the dynamic relationship between insider trading and markets returns and

test the above-mentioned hypotheses.

B. Estimating one-period expected returns, cash flow news and discount rate news

We follow Campbell (1991), and Campbell and Vuolteenaho (2004) to estimate

the one-period expected return, cash flow news, and discount rate news series using a

vector autoregressive (VAR) model. We assume that the data are generated by a

first-order VAR model

Zt+1 = A0 +AZt + ut+1 (6)

where Zt+1 is a vector of excess log market returns, the term yield defined as the yield

difference between ten-year constant-maturity taxable bonds and short-term taxable

notes, the price-earnings ratio from S&P 500 index, and small-value spreadb,

b For details of data construction, see Campbell and Vuolteenaho (2004)

14

describing the economy at time t+1, A0 and A are vector and matrix of constant

parameters, and ut+1 is a vector of shocks. With the VAR expressed in this form, the

components of identity (1) can be obtained by

EtRt+1 = e1’(A0 + AZt) (7a)

NCF,t+1 = [e1’ + e1’ρA(I-ρA)-1]ut+1 (7b)

-NDR,t+1 = e1’ ρA(I-ρA)-1ut+1 (7c)

Where e1’ = [1 0 … 0], and I is an identity matrix. Equation (7) expresses EtRt+1, the

one-period expected return as fitted value of Zt+1 based on VAR model in equation

(3), NCG,t+1, the cash-flow news, and NDR,t+1, the discount rate news as linear functions

of the t+1 shock vectors..

IV. Data and Summary Statistics.

A. VAR data

In order to decompose the realized return into expected return, cash flow news

and discount rate news using VAR approach, we need to specify variables to be

included in the state vector. Following Campbell and Vuolteenaho (2004), we choose

a model with the following four state variables: the excess market return ( measured

as the log excess return on the Center for Research Security Prices [CRSP] value-

weighted index over Treasury bills; the term yield spread between long-term and

short-term bonds (measured as the difference between ten-year constant-maturity

taxable bond yield and the yield on short-term taxable notes); the market’s price-

earnings ratio (measured as the log ratio of the S&P 500 price index to a ten-year

moving average of S&P 500 earnings); and small-stock value spread (measured as the

15

difference between the log book-to-market ratios of small value and small growth

stocks). Asset pricing literature finds that these state variables are able to forecast

and track aggregate market returnsc.

B. Insider trading data

We collect all insiders trading information from the Securities Exchange

Commission (SEC) Ownership Reporting System (ORS). The ORS data starts in

1975 and ends in 2000 and contains all insider transaction data that are subject to

disclosure by the Securities Exchange Act of 1934. Section 16(a) of the Act requires

that open market trades by corporate insiders be reported to SEC within 10 days after

the end of month in which they took place. For the purposes of this reporting

requirement, “corporate insiders” include officers with decision making authorities

over the operations of the company (CEOs, CFOs, other officers, presidents, vice-

presidents etc), all members of the board of directors, and beneficial owners of more

than 10% of the company’s stock. These reports filed on the SEC’s Form 3, 4 and 5

are the source of insider trading data. From the reported transactions we exclude all

transactions that are less than 100 shares and only focus on open market purchases

and sales by insiders.

Using the ORS data we classify insiders into three groups. The first group,

Management, includes Chairmen of the board, CEO, CFO, Officers, Directors,

Presidents, and Vice-Presidents and is assumed to have direct access to information

about the firm’s future prospects. ‘Large shareholders’ are that who are not

management but owns 10% or more of shares and are assumed to have no direct

c We do not incorporate inside trading into the VAR on purpose, because our null hypothesis is that inside trading is not informative.

16

access to inside information. The third group ‘others’ are all investors who are

required to report their trades to SEC bur are neither managers nor large shareholders.

We define a measure of aggregate insider trading activity, IT in the following

manner. For each quarter in our sample from January 1978 to December 2000 we

designate a firm to be an insider buy (sell) if the number of insiders buying (selling) is

greater than the number of insiders selling (buying) in that month. For each quarter

IT is defined as the net buys to total number of buys and sells in that quarter.

In Table I we present summary statistics of the trading behavior of insiders

during our sampled period. On average, for the total sample, there are 1.017 insider

buying stocks in their firm per quarter and 1.118 insiders selling. For the management

group, there are 0.778 number of buy per quarter and 1.432 numbers of sells. For the

large shareholders group, there are 1.21 buys per quarter and 0.794 sells. When we

look at the trading behavior across the size of the firms we notice a monotonic

decrease in buys and a monotonic increase in sales for the management group. Buys

decrease from .977 per quarter in the small firms to .622 per quarter in the large firms

for the management group. Sales range from .796 per quarter in the small firms to

2.122 per quarter in the large firms. These results are in line with previous evidence

of insiders buying more heavily in smaller firms and selling heavily in larger firms

(Seyhun (1986), Rozeff and Zaman (1988)).

V. Results

The evidence presented in this section uses a VAR model to examine the

relationship between aggregate insider trading and aggregate market return. We

17

regress realized market excess returns (defined as the CRSP value-weighted return

minus three month T-Bill rates) and its three estimated components (one-period

expected market excess return, cash flow news and negative of discount rate news)

individually on lagged values of aggregate insider trading measure, IT. The return

decomposition is based on the VAR system in equation (7). We report the estimates,

t-statistics, adjusted R2, sum of γ and Granger causality test in Table II. T-statistics

are computed using Newey-West heteroskedastic-robust standard errors with 5 lags,

and are list below each estimate in parentheses. F-test is the Granger Causality test

that the coefficients of all lagged insider trading are zero.d. The P-value is listed

below the F-test in bracket. In Panel A we report results for all insiders. First row of

Panel A shows that trading by all insiders has no explanatory power in explaining the

variation in realized market returns. The F-statistic is 5.386 with a p-value of 0.146.

Note that the F-statistic is used for the Granger-causality test of whether the

coefficients of lagged IT explain the variation in Rt. Furthermore, none of the

individual coefficients of lagged IT are significant at the 5% confidence level. This

suggests that there is no relation between realized market return and insider trading.

However, as we discussed in Section III such a lack of relationship does not

necessarily imply insider trading is not informative. The second, third and fourth rows

report results when the realized excess return is decomposed into one-period expected

return, cash flow news and the discount rate news. The F-statistic for the expected

return, cash flow news and discount rate news are 6.145, 12.686, and 2.362 with p-

values of 0.105, 0.005 and 0.501 respectively. Our results suggest the following.

d For the sake of brevity, we only report estimates of coefficients of variables which are of interest. For example, in Table II, we only report the estimates of lagged IT, the γi’s. The adjusted R2 reported is for the full model which includes the lagged X variables.

18

When insiders are viewed as a monolithic group, their trading has no effect on

realized market return. However, if we decompose the realized return into three

components, insider trading is positively related to future aggregate cash flow news.

In other words, insider trading can explain variations in realized market return which

is due to future unexpected cash flow news effect. Also, the one-quarter-lagged IT

coefficient for the NEWS regression (sum of cash flow news and discount rate news)

has a coefficient of 0.062 with a t-statistic of 2.29. These results suggest that sum of

unexpected cash flow news and discount rate news experience significant positive

shocks subsequent to insiders buying stocks in their firms. For the other lagged IT

the coefficients are not significant.

As defined before the Management group comprises of insiders who are assumed to

have direct access to information about the firm’s future prospects. If this is true then

managerial timing hypothesis would predict a stronger relation between insider

trading and future aggregate market returns. Panel B reports results for the

Management group. Here the effect of insider trading is more pronounced as

predicted by the managerial timing hypothesis. The F-statistics for realized return,

expected return and cash flow news are 7.926, 10.335, and 17.651 which are all

significant at the 5 per cent level. The one quarter lagged coefficients of IT for the

realized return, expected return, cash flow news, discount rate news and NEWS

regressions are 0.029, 0.008, 0.039, -0.002 and 0.04 with t-statistics of 1.64, 2.32,

2.82, -0.26 and 2.85 respectively. This provides strong evidence that insiders who are

directly related to the day-to-day activities of the firms are better able to predict

aggregate market return. The 2-quarter and 3-quarter lagged IT coefficients are not

19

significant. Furthermore, our results show that trading by this group of insiders is

more likely to be related to unexpected future cash flow news one quarter later.

These results are quite different than what Chowdhury, Howe and Lin (1993) and

Lakonishok and Lee (2001) claim in their paper.

An interesting fact also emerges when comparing Panel A and panel B results. In

Panel A, insider trading does not explain the variation of the expected return of the

aggregate market (p-value for the F-test is 0.105). In contrast, in Panel B which

considers the Management group, insider trading explains some variation in the

expected return of the aggregate market (p-value for the F-test is 0.016). This

suggests that when managers trade the market revises its expectations about the

future.

Panel C reports results for the large shareholders group. F-statistics for expected

return and cash flow news are marginally significant and the lagged coefficient for IT

is only significant in the NEWS regression. For this group there is marginal evidence

that trading is positively related to future unexpected news. Also, the 2 quarter and 3

quarter lagged coefficients for IT are all insignificant.

In Table III we report results of regressions between insider trading and realized

market return and the components of realized market return. Here the insider trading

variable, IT is the dependent variable. The motivation here is to analyze whether it is

the market’s expectations of return that is driving insider trading and thereby supports

Chowdhury, Howe and Lin (1993) assertion of contrarian strategy. In this part of our

analysis, we are more interested in the relation between insider trading and the lagged

values of one-period expected market excess returns. If the assertion of contrarian

20

strategy is true then we should expect a negative relation between insiders trading and

lagged expected return.

In panel A, like before we report results for the overall group of insiders. The F-

statistics are 4.35, 3.63, 3.56, 4.16 and 5.96 when realized market excess returns,

expected market excess returns, cash flow news, discount rate news and total news

are regressed on lagged values of insider trading, respectively. None of the

coefficients of the insider trading variable IT is significant. The coefficient of one-

quarter lagged IT is 2.034 with a t-statistic of 0.73 in the expected return regression.

Even though the sum of coefficients of lagged expected returns is negative as the

contrarian strategy would predict the F-test shows that this relationship is statistically

insignificant. Panel A suggests a lack of evidence in support of the contrarian

strategy. In Panel B and Panel C we report results for the Management group and the

large shareholders group. Here again the evidence does not support the contrarian

strategy. The coefficient for lagged IT in the expected return regressions are 1.275

with a t-statistic of 0.25 and 1.292 with a t-statistic of 0.62 for the Management and

Large shareholders group respectively. Also, none of the F-statistics are significant.

Evidence presented in this table clearly shows that aggregate market returns do not

cause insider trading and insider trading is not a manifestation of the contrarian

strategy.

These results are quite different than what Chowdhury, Howe and Lin (1993) and

Lakonishok and Lee (2001) claim in their papers. Both papers conclude that insiders’

trade are more likely to be a function of contrarian strategy. These studies did not

address the issue of whether the observed relationship between insiders trading and

21

aggregate market returns could be due to managerial timing. In this paper we directly

test both hypotheses of managerial timing and contrarian strategy by using return

decomposition methods. Our results, however, show that insider trading is more

likely to be based on managers’ ability to time the market based on superior

information. Table II provides evidence that insider trading is related to future cash

flow news and hence is more likely to be based on the managers’ ability to predict

market wide activities and Table III shows no relation between insider trading and

lagged expected return, implying a lack of evidence in support of the contrarian

strategy.

VI. Firm Size, Information Uncertainty and Aggregate Insider Trading

Jiang, Lee and Zhang (2004) define information uncertainty as the degree to which

a firm’s value can be estimated by the most knowledgeable investors at reasonable

costs. Using this definition, high information uncertainty firms would be those firms

whose expected cash flows may be difficult to estimate due to their environment or

nature of operations etc. These firms are likely to have high information acquisition

costs and their fundamental values are more likely to be unreliable and volatile. If

aggregate insider trading is driven by the contrarian strategy then insiders are more

likely to trade in high information uncertainty firms as these are more likely to have

current market values deviating from the ‘true’ fundamental values. On the other

hand, low information uncertainty firms are more likely to have market values equal

to the fundamental values. Insider trading in these types of firms is more likely to be

22

a manifestation of the insider’s ability to predict aggregate market return based on

managerial timing rather than contrarian strategy.

To the extent that small firms have high information acquisition costs and are

likely to be followed by fewer analysts we use firm size as a proxy for information

uncertainty.

For each quarter in our insider trading sample we form size quintiles based on the

market capitalization value. The first quintile comprises of small firms while the fifth

quintile comprises of large firms. We repeat our earlier analyses on smaller firms and

larger firms but confine it to the Management group of insiders. In Table IV we

report regression results for the two groups-small firms and large firms. Realized

market excess returns and its components are regressed on lagged values of IT for the

small firm and large firm samples. Panel A reports results for small firms. The F-

statistics for realized return, expected return, cash flow news, discount rate news and

news regressions are 7.24, 6.294, 10.156, 5.098 and 16.049 respectively. In Panel B

results for large firms are reported. Here the F-statistics for realized returns, expected

return, cash flow news, discount rate news and news are 7.324, 4.77, 15.365, 2.311

and 18.942 respectively. The results suggest that for both small and large firms,

insider trading is positively related and predicts the realized market returns, even

though the results are marginally significant. There is a negative, marginally

significant relation between insider trading and expected return for the small firms.

For both type of firms insider trading and cash flow news are positively and

significantly related suggesting that aggregate insider trading predicts future cash

flow news.

23

Table V reports results when IT is regressed on lagged values of realized returns,

expected returns, cash flow news, discount rate news and total news for both the

small firm group and the large firm group. In Panel A, for small firms the F-statistics

for the regressions of realized returns, expected returns, cash flow news, discount rate

news and total news are 4.07, 15.68, 3.7, 2.35, and 6.56 respectively. The only

regression suggesting causality is when IT is regressed on expected returns. The

evidence suggests that expected return is negatively related to insider trading and

insider trading in this type of firms is more likely due to contrarian strategy. In Panel

B none of the regressions are significant for the large firms.

Our results from Table IV and V confirm what we conjectured earlier regarding

information uncertainty and insider trading. We find that for firms which are low in

information uncertainty (large firms) insider trading is more likely due to managerial

timing whereas for firms which have high information uncertainty (small firms)

insider trading is more likely because of contrarian strategy. Seyhun (1988) finds that

insiders of only larger firms are more likely to observe and trade on the basis of

economywide factors that affect their firms. Our results confirm Seyhun’s findings.

In addition we show that insiders of both large and small firms trade on the basis of

future cash flow news while only insiders in small firms are likely to trade because of

contrarian investment strategy.

VII. Conclusion

Evidence from recent research has separately shown that insiders are able to time

the market on the basis of contrarian beliefs (e.g., Rozeff and Zaman, 1998), on the

24

basis of superior knowledge about future cash flow news (e.g., Ke et al., 2003).

Piotroski and Roulstone (2005) document that insiders trade on the basis of both

contrarian beliefs and superior knowledge. In this study we examine the ability of

aggregate insider trading to predict market-wide movement using return

decomposition in a vector autoregressive (VAR) model framework. This approach of

return decomposition is different from earlier studies of aggregate insider trading.

We decompose market returns into expected return, unexpected cash flow news and

unexpected discount rate news by closely following the methods outlined in

Campbell (1991). Such decomposition enables us to identify the source of

predictability of aggregate insider trading. We argue that if insiders are trading on the

basis of superior information then aggregate insider trading are more likely to be

positively related to unexpected cash flow news. On the other hand, if these trades

are a result of contrarian beliefs then insider trading should be negatively related to

past expected return. We find strong evidence that aggregate insider trading is

positively related to unexpected cash flow news for all types of insiders. When we

partition our sample based on insiders who are more likely to have access to

performance related information these results are much stronger and significant. We

also examine whether aggregate insider trading is in response to market expectations.

We find no evidence of aggregate insider trading is caused by market expectations.

Our results strongly suggest that insiders are able to predict market return because of

having superior information about future cash flow news. In other words the market

timing ability of aggregate insider trading is due to managerial timing and not due to

contrarian strategy.

25

To further reinforce our results we classify firms into high information uncertainty

and low information uncertainty firm and use firm size as a proxy for information

uncertainty. If aggregate insider trading is due to contrarian strategy then insiders are

more likely to trade in small firms and if insiders trade due to managerial timing then

these trades should be concentrated in larger firms. We find that the predictive ability

of aggregate insider trading for both large and small firms is due to managerial

timing. However, we also find evidence of aggregate insider trading in small firms to

be associated with contrarian strategy of investment.

The fact that insider trading is due to managerial timing has an important

implication. Given that insider trades are driven by superior information aggregate

insider trading should therefore be construed as a leading indicator of market wide

activities. Furthermore, such trading by insiders will drive prices towards

fundamental values.

26

References

Baker, Malcolm, Ryan Taliaferro, and Jeffrey Wurgler, 2006, Predicting returns with managerial decision variables: Is there a small sample bias? Journal of Finance (forthcoming).

Baker, Malcolm, and Jeffrey Wurgler, 2000. The equity share in new issues and aggregate stock returns, Journal of Finance 55, 2219–2257.

Campbell, J. Y., 1991.A variance decomposition for stock returns, Economic Journal 101, 157-179.

Campbell, J. Y., T. Vuolteenaho, 2004.Bad Beta, Good Beta, American Economic Review 94, 1249-1275...

Cohen, R., P. Gamers, and T. Voulteenaho, 2002. Who under reacts to cash-flow news? Evidence from trading between individuals and institutions. The Journal of Financial Economics 66, 409-506.

DeLong B., A. Shleifer, L. Summers, and R. Waldmann 1990. Positive feedback investment strategies and destabilizing rational speculation, Journal of Finance 45, 374-397.

Fama, E., French, K., 1992. The cross-section of expected stock returns. The Journal of Finance 47, 427–465.

Hecht, P., T. Vuolteenaho, 2006. Explaining returns with cash-flows proxies, The Review of Financial Studies 19, 159-194.

Ikenberry, D., J. Lakonishok, and T. Vermaelen, 1995. Market under reaction to open market shares repurchase, Journal of Financial Economics 39, 181-208.

Jeng, L., A. Metrick, and R. Zeckhauser, 2003. Estimating the returns to insider trading: a performance-evaluation perspective. The Review of Economics and Statistics 85, 453-471.

Jiang G., C. Lee and G. Zhang, 2004. Information uncertainty and expected returns, Cornell University Working paper.

Ke, B., Huddart, S. and Petroni, K., 2003. What insiders know about future earnings and how they use it: evidence from insider trades, Journal of Accounting and Economics 35, 315–346

Lakonishok, J., Lee, I., 2001. Are insider trades informative? The Review of Financial Studies 14, 79–111.

27

Loughran, Tim, and Jay R. Ritter, 1995. The new issues puzzle, Journal of Finance 50, 23–51.

Meulbroek, L., 1992. An empirical analysis of illegal insider trading, The Journal of Finance 47, 1661–1699.

Piotroski, J.D., D.T. Roulstone, 2005. Do insider trades reflect both contrarian beliefs and superior knowledge about future cash flow realization? Journal of Accounting and Economics 39, 55–81.

Rozeff, M., Zaman, M., 1988. Market efficiency and insider trading: new evidence. The Journal of Business 61, 25–44.

Rozeff, M., Zaman, M., 1998. Overreaction and insider trading: evidence from growth and value portfolios. The Journal of Finance 53, 701–716.

Seyhun, H.N., 1986. Insider’s profits, cost of trading, and market efficiency. Journal of Financial Economics 16, 189–212.

Seyhun, H.N., 1988. The Information Content of Aggregate Insider Trading.The Journal of Business 61, 1-24.

Seyhun, H.N., 1992. Why does aggregate insider trading predict future stock returns? Quarterly Journal of Economics 107, 1303–1331.

Seyhun, H.N., Bradley, M., 1997. Corporate bankruptcy and insider trading. The Journal of Business 70, 189–216.

Shiller, Robert J., 1984. Stock prices and social dynamics, Brookings Papers on Economic Activity, 457-498

28

Table ISummary Statistics

This table summarizes the statistics of insider trading for all open market purchases and sales of NYSE/AMEX and Nasdaq CRSP- and Compustat-listed common shares (CRSP share code 10 or 11) during 1978:Q1 to 2000:Q4. We report average quarterly number of buys and sells per firm of our sample. We exclude all option transactions and transactions less than 100 shares. We define “Management: as CEOs, CFOs, and chairmen of the board, directors, officers, presidents, and vice presidents. “Large shareholders” are those who own more than 10% of shares and are not in management. “Others” are all those who are required to report their trading to the SEC but neither managers nor large shareholders. Large, medium, and small firms are firms based on the sample firms’ quintile cutoff points at the market value in previous quarter.

Management Large shareholders Others TotalBuys Sales Buys Sales Buys Sales Buys Sales

All 0.778 1.432 1.210 0.794 1.121 1.108 1.017 1.118Small firms 0.977 0.796 1.203 0.562 1.182 0.638 1.110 0.670Medium firms 0.748 1.332 1.155 0.780 1.064 1.080 0.973 1.062Large firms 0.622 2.122 1.276 1.053 1.113 1.629 0.967 1.621

29

Table II Managerial Timing Test

This table shows the results of the regressions between insider trading and market return (its components) over the period 1978:Q1-2000:Q4. IT denotes the insider trading in equation 3, Rt denotes the realized market excess return, Et-1[Rt] denotes the expected market excess return, NCFt denotes the cash flow news, NDRt denotes the discount rate news, NEWS denotes the sum of cash flow news and discount rate news and D is a dummy variable to control for seasonality. The return decomposition is based on the VAR system in equation 7. We report the estimates, t-statistics, adjusted R2, sum of γ (β) in Panel A (B), and Granger causality test. Note that the adjusted R2 is for the full model whereas in the table we only report the coefficients of lagged IT. T-statistics are computed using Newey-West heteroskedastic-robust standard errors with 5 lags, and are list below each estimate in parentheses. F-test is the Granger Causality test that the coefficients of all lagged insider trading are zero. The P-value is listed below the F-test in bracket.

Panel A: All Insiders

ITt-1 ITt-2 ITt-3 R2 F-test

Rt 0.042 0.004 0.036 0.003 0.081 5.386

(1.195) (0.091) (0.929) [0.146]

Et-1[Rt] 0.015 -0.013 -0.006 0.843 -0.004 6.145

(1.796) (-1.733) (-1.401) [0.105]NCFt 0.052 0.031 0.044 0.065 0.127 12.686

(1.561) (0.727) (1.159) [0.005]-NDRt 0.007 0.001 0.009 0.042 0.018 2.362

(0.393) (0.076) (0.601) [0.501]

NEWSt 0.062 0.018 0.059 0.104 0.139 17.770

(2.294) (0.577) (1.555) [0.000]

Panel B: Management

Rt 0.029 0.007 0.014 0.010 0.049 7.926

(1.637) (0.394) (0.861) [0.048]

Et-1[Rt] 0.008 -0.008 -0.002 0.845 -0.002 10.335

(2.323) (-2.892) (-0.973) [0.016]NCFt 0.039 0.023 0.012 0.077 0.074 17.651

(2.823) (1.283) (0.748) [0.001]-NDRt -0.002 -0.001 0.012 0.059 0.009 2.584

(-0.260) (-0.156) (1.291) [0.460]

NEWSt 0.040 0.014 0.028 0.109 0.082 25.502

(2.850) (0.866) (1.753) [0.000]

Panel C: Large Shareholders

Rt 0.035 -0.023 0.056 -0.014 0.068 3.487

(0.910) (-0.434) (1.148) [0.322]

Et-1[Rt] 0.016 -0.010 -0.009 0.840 -0.003 7.459

(1.824) (-1.095) (-1.692) [0.059]NCFt 0.040 0.016 0.065 0.039 0.120 8.251

(0.969) (0.271) (1.419) [0.041]-NDRt 0.012 -0.001 0.008 0.044 0.020 2.404

(0.578) (-0.031) (0.485) [0.493]

NEWSt 0.058 -0.004 0.079 0.079 0.133 13.028

(1.953) (-0.083) (1.658) [0.005]

30

Table III Contrarian Strategy Test

This table shows the results of the regressions between insider trading and market return (its components) over the period 1978:Q1-2000:Q4. IT denotes the insider trading in equation 5, Rt denotes the realized market excess return, Et-1[Rt] denotes the expected market excess return, NCFt denotes the cash flow news, NDRt denotes the discount rate news, NEWS denotes the sum of cash flow news and discount rate news and D is a dummy variable to control for seasonality. The return decomposition is based on the VAR system in equation 7. We report the estimates, t-statistics, adjusted R2, sum of γ (β) in Panel A (B), and Granger causality test. Note that the adjusted R2 is for the full model whereas in the table we only report the coefficients of lagged X. T-statistics are computed using Newey-West heteroskedastic-robust standard errors with 5 lags, and are list below each estimate in parentheses. F-test is the Granger Causality test that the coefficients of all lagged return are zero. The P-value is listed below the F-test in bracket.

Panel A: All Insiders

Xt-1 Xt-2 Xt-3 R2 F-test

X=Rt -0.003 -0.244 0.520 0.355 0.273 4.353

(-0.007) (-0.412) (1.806) [0.226]

X=Et-1[Rt] 2.034 -3.343 -0.560 0.364 -1.869 3.637

(0.730) (-1.174) (-0.299) [0.303]

X=NCFt 0.273 -0.284 0.436 0.356 0.425 3.564

(0.850) (-0.502) (1.803) [0.313]

X=-NDRt -0.820 1.067 1.697 0.379 1.945 4.156

(-0.861) (1.192) (1.992) [0.245]

X=NEWSt 0.059 -0.153 0.610 0.363 0.516 5.956

(0.170) (-0.305) (2.223) [0.114]

Panel B: Management

X=Rt 0.700 -0.370 1.158 0.280 1.488 3.565

(0.851) (-0.369) (1.869) [0.312]

X=Et-1[Rt] 1.275 -5.214 0.208 0.276 -3.731 4.752

(0.248) (-1.123) (0.063) [0.191]

X=NCFt 1.120 -0.494 0.858 0.290 1.483 4.587

(1.418) (-0.505) (1.698) [0.205]

X=-NDRt -0.813 2.159 3.238 0.288 4.585 3.691

(-0.526) (1.177) (1.890) [0.297]

X=NEWSt 0.809 -0.286 1.196 0.294 1.720 4.942

(1.045) (-0.319) (2.100) [0.176]

Panel C: Large Shareholders

X=Rt -0.067 0.003 0.463 0.401 0.399 4.036

(-0.195) (0.005) (1.765) [0.258]

X=Et-1[Rt] 1.292 -1.788 -1.216 0.407 -1.712 2.183

(0.624) (-0.767) (-0.701) [0.535]

X=NCFt 0.091 -0.045 0.355 0.395 0.401 3.007

(0.327) (-0.100) (1.522) [0.391]

X=-NDRt -0.335 1.071 1.653 0.425 2.389 4.807

(-0.388) (1.473) (2.100) [0.186]

X=NEWSt -0.046 0.051 0.522 0.410 0.528 5.448

(-0.161) (0.116) (1.875) [0.142]

31

Table IV Managerial Timing Test for Different Size Portfolios

This table shows the results of the regressions between insider trading and market return (its components) over the period 1978:Q1-2000:Q4. Small firms is the lowest quintile of the sample firms’ market capitalization and large firms is the highest quintile. IT denotes the insider trading in equation 3, Rt denotes the realized market excess return, Et-1[Rt] denotes the expected market excess return, NCFt denotes the cash flow news, NDRt denotes the discount rate news, NEWS denotes the sum of cash flow news and discount rate news and D is a dummy variable to control for seasonality. The return decomposition is based on the VAR system in equation 7. We report the estimates, t-statistics, adjusted R2, sum of γ (β) in Panel A (B), and Granger causality test. Note that the adjusted R2 is for the full model whereas in the table we only report the coefficients of lagged IT. T-statistics are computed using Newey-West heteroskedastic-robust standard errors with 5 lags, and are list below each estimate in parentheses. F-test is the Granger Causality test that the coefficients of all lagged insider trading are zero. The P-value is listed below the F-test in bracket.

Panel A: Small firms

ITt-1 ITt-2 ITt-3 R2 F-testRt 0.031 -0.010 0.072 0.015 0.093 7.240

(0.788) (-0.230) (2.079) [0.065]Et-1[Rt] 0.010 -0.010 -0.006 0.834 -0.006 6.294

(1.131) (-1.217) (-1.204) [0.098]NCFt 0.047 0.012 0.096 0.087 0.155 10.156

(1.441) (0.256) (2.389) [0.017]-NDRt 0.022 0.006 -0.006 0.066 0.022 5.098

(1.514) (0.297) (-0.418) [0.165]NEWSt 0.071 0.005 0.094 0.130 0.170 16.049

(2.314) (0.153) (2.623) [0.001]Panel B: Large firmsRt 0.044 0.005 0.023 0.018 0.072 7.324

(2.350) (0.239) (0.926) [0.062]Et-1[Rt] 0.009 -0.009 -0.001 0.835 -0.001 4.770

(1.627) (-1.708) (-0.450) [0.189]NCFt 0.055 0.017 0.017 0.044 0.089 15.365

(2.740) (0.755) (0.674) [0.002]-NDRt 0.000 0.003 0.012 0.049 0.015 2.311

(-0.008) (0.246) (0.794) [0.510]NEWSt 0.055 0.011 0.032 0.075 0.099 18.942

(3.723) (0.647) (1.273) [0.000]

32

Table V Contrarian Strategy for Different Size Portfolios

This table shows the results of the regressions between insider trading and market return (its components) over the period 1978:Q1-2000:Q4. Small firms are the lowest quintile of the sample firms’ market capitalization and large firms are the highest quintile. IT denotes the insider trading in equation 5, Rt denotes the realized market excess return, Et-1[Rt] denotes the expected market excess return, NCFt denotes the cash flow news, NDRt denotes the discount rate news, NEWS denotes the sum of cash flow news and discount rate news and D is a dummy variable to control for seasonality. The return decomposition is based on the VAR system in equation 7. We report the estimates, t-statistics, adjusted R2, sum of γ (β) in Panel A (B), and Granger causality test. Note that the adjusted R2 is for the full model whereas in the table we only report the coefficients of lagged X. T-statistics are computed using Newey-West heteroskedastic-robust standard errors with 5 lags, and are list below each estimate in parentheses. F-test is the Granger Causality test that the coefficients of all lagged return are zero. The P-value is listed below the F-test in bracket.

Panel A: Small firms

ITt-1 ITt-2 ITt-3 R2 F-testX=Rt -0.453 -0.468 0.137 0.353 -0.784 4.070

(-1.252) (-0.842) (0.556) [0.254]X=Et-1[Rt] 1.831 -2.330 -3.134 0.419 -3.632 15.678

(0.696) (-0.977) (-1.701) [0.001]X=NCFt -0.041 -0.237 0.303 0.331 0.025 3.703

(-0.171) (-0.462) (1.911) [0.295]X=-NDRt -0.771 0.787 1.498 0.359 1.514 2.350

(-0.929) (0.969) (1.440) [0.503]X=NEWSt -0.196 -0.163 0.464 0.343 0.106 6.556

(-0.663) (-0.337) (2.359) [0.087]Panel B: Large firmsX=Rt 0.163 -0.435 0.640 0.270 0.367 3.963

(0.337) (-0.554) (1.841) [0.265]X=Et-1[Rt] 3.726 -4.857 0.294 0.264 -0.837 1.883

(0.885) (-1.100) (0.133) [0.597]X=NCFt 0.495 -0.730 0.534 0.285 0.300 4.054

(1.410) (-0.935) (1.632) [0.256]X=-NDRt -1.411 1.963 1.901 0.308 2.452 3.828

(-1.079) (1.667) (1.731) [0.281]X=NEWSt 0.133 -0.417 0.685 0.271 0.401 4.334

(0.315) (-0.601) (2.026) [0.228]

33